mirror of
https://github.com/invoke-ai/InvokeAI
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108 lines
4.8 KiB
Python
108 lines
4.8 KiB
Python
import os
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from builtins import float
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from typing import List, Union
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from pydantic import BaseModel, ConfigDict, Field
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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Input,
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InputField,
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InvocationContext,
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OutputField,
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UIType,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.primitives import ImageField
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from invokeai.backend.model_management.models.base import BaseModelType, ModelType
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from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
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class IPAdapterModelField(BaseModel):
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model_name: str = Field(description="Name of the IP-Adapter model")
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base_model: BaseModelType = Field(description="Base model")
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model_config = ConfigDict(protected_namespaces=())
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class CLIPVisionModelField(BaseModel):
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model_name: str = Field(description="Name of the CLIP Vision image encoder model")
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base_model: BaseModelType = Field(description="Base model (usually 'Any')")
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model_config = ConfigDict(protected_namespaces=())
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class IPAdapterField(BaseModel):
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image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
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ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
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image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
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weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
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# weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = Field(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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@invocation_output("ip_adapter_output")
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class IPAdapterOutput(BaseInvocationOutput):
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# Outputs
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ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
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@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
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class IPAdapterInvocation(BaseInvocation):
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"""Collects IP-Adapter info to pass to other nodes."""
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# Inputs
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image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
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ip_adapter_model: IPAdapterModelField = InputField(
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description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
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)
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# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
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weight: Union[float, List[float]] = InputField(
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default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
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)
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begin_step_percent: float = InputField(
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default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = InputField(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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def invoke(self, context: InvocationContext) -> IPAdapterOutput:
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# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
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ip_adapter_info = context.services.model_manager.model_info(
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self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
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)
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# HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
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# directly, and 2) we are reading from disk every time this invocation is called without caching the result.
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# A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
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# is currently messy due to differences between how the model info is generated when installing a model from
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# disk vs. downloading the model.
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image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
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os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
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)
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image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
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image_encoder_model = CLIPVisionModelField(
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model_name=image_encoder_model_name,
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base_model=BaseModelType.Any,
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)
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return IPAdapterOutput(
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ip_adapter=IPAdapterField(
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image=self.image,
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ip_adapter_model=self.ip_adapter_model,
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image_encoder_model=image_encoder_model,
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weight=self.weight,
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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),
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)
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